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Computerised electrocardiogram classification

Mahrousa, Zakria Zaki 2004. Computerised electrocardiogram classification. PhD Thesis, Cardiff University.

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Abstract

Advances in computing have resulted in many engineering processes being automated. Electrocardiogram (ECG) classification is one such process. The analysis of ECGs can benefit from the wide availability and power of modern computers. This study presents the usage of computer technology in the field of computerised ECG classification. Computerised electrocardiogram classification can help to reduce healthcare costs by enabling suitably equipped general practitioners to refer to hospital only those people with serious heart problems. Computerised ECG classification can also be very useful in shortening hospital waiting lists and saving life by discovering heart diseases early. The thesis investigates the automatic classification of ECGs into different disease categories using Artificial Intelligence (AI) techniques. A comparison of the use of different feature sets and AI classifiers is presented. The feature sets include conventional cardiological features, as well as features taken directly from time domain samples of an ECG. The benchmark AI classifiers tested include those based on neural network, k-Nearest Neighbour and inductive learning techniques. The research proposes two modifications to the learning vector quantisation (LVQ) neural network, namely the All Weights Updating-LVQ (AWU-LVQ) algorithm and the Neighbouring Weights Updating-LVQ (NWU-LVQ) algorithm, yielding an "intelligent" diagnostic heart system with higher accuracy and reduced training time compared to existing AI techniques.

Item Type: Thesis (PhD)
Status: Unpublished
Schools: Engineering
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
T Technology > TC Hydraulic engineering. Ocean engineering
ISBN: 9781303200311
Funders: Aleppo University
Date of First Compliant Deposit: 30 March 2016
Last Modified: 12 Feb 2016 23:15
URI: http://orca-mwe.cf.ac.uk/id/eprint/55932

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